import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__a    :	Any         =    logging.get_logger(__name__)




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					List[Any]     					=		['''input_features''', '''is_longer''']





			def __init__(  self ,  lowerCAmelCase__=64 ,  lowerCAmelCase__=4_80_00 ,  lowerCAmelCase__=4_80 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=False ,  lowerCAmelCase__ = 0 ,  lowerCAmelCase__ = 1_40_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "fusion" ,  lowerCAmelCase__ = "repeatpad" ,  **lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__(
							    feature_size=lowerCAmelCase__ ,  sampling_rate=lowerCAmelCase__ ,  padding_value=lowerCAmelCase__ ,  return_attention_mask=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )
							__lowercase					     =				top_db
							__lowercase					     =				truncation
							__lowercase					     =				padding
							__lowercase					     =				fft_window_size
							__lowercase					     =				(fft_window_size >> 1) + 1
							__lowercase					     =				hop_length
							__lowercase					     =				max_length_s
							__lowercase					     =				max_length_s * sampling_rate
							__lowercase					     =				sampling_rate
							__lowercase					     =				frequency_min
							__lowercase					     =				frequency_max
							__lowercase					     =				mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins ,  num_mel_filters=lowerCAmelCase__ ,  min_frequency=lowerCAmelCase__ ,  max_frequency=lowerCAmelCase__ ,  sampling_rate=lowerCAmelCase__ ,  norm=lowerCAmelCase__ ,  mel_scale='''htk''' ,  )
							__lowercase					     =				mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins ,  num_mel_filters=lowerCAmelCase__ ,  min_frequency=lowerCAmelCase__ ,  max_frequency=lowerCAmelCase__ ,  sampling_rate=lowerCAmelCase__ ,  norm='''slaney''' ,  mel_scale='''slaney''' ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict[str, Any]:

							'''simple docstring'''


							__lowercase					     =				copy.deepcopy(self.__dict__		)
							__lowercase					     =				self.__class__.__name__
							if "mel_filters" in output:
											del output["mel_filters"]
							if "mel_filters_slaney" in output:
											del output["mel_filters_slaney"]
							return output





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   np.ndarray:

							'''simple docstring'''


							__lowercase					     =				spectrogram(
							    lowerCAmelCase__ ,  window_function(self.fft_window_size ,  '''hann'''		) ,  frame_length=self.fft_window_size ,  hop_length=self.hop_length ,  power=2.0 ,  mel_filters=lowerCAmelCase__ ,  log_mel='''dB''' ,  )
							return log_mel_spectrogram.T





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				np.array_split(list(range(0 ,  total_frames - chunk_frames + 1		)		) ,  3		)
							if len(ranges[1]		) == 0:
											# if the audio is too short, we just use the first chunk
											__lowercase					     =				[0]
							if len(ranges[2]		) == 0:
											# if the audio is too short, we just use the first chunk
											__lowercase					     =				[0]
							# randomly choose index for each part
							__lowercase					     =				np.random.choice(ranges[0]		)
							__lowercase					     =				np.random.choice(ranges[1]		)
							__lowercase					     =				np.random.choice(ranges[2]		)

							__lowercase					     =				mel[idx_front : idx_front + chunk_frames, :]
							__lowercase					     =				mel[idx_middle : idx_middle + chunk_frames, :]
							__lowercase					     =				mel[idx_back : idx_back + chunk_frames, :]

							__lowercase					     =				torch.tensor(mel[None, None, :]		)
							__lowercase					     =				torch.nn.functional.interpolate(
							    lowerCAmelCase__ ,  size=[chunk_frames, 64] ,  mode='''bilinear''' ,  align_corners=lowerCAmelCase__		)
							__lowercase					     =				mel_shrink[0][0].numpy()
							__lowercase					     =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,  axis=0		)
							return mel_fusion





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   np.array:

							'''simple docstring'''


							if waveform.shape[0] > max_length:
											if truncation == "rand_trunc":
															__lowercase					     =				True
															# random crop to max_length (for compatibility) -> this should be handled by self.pad
															__lowercase					     =				len(lowerCAmelCase__		) - max_length
															__lowercase					     =				np.random.randint(0 ,  overflow + 1		)
															__lowercase					     =				waveform[idx : idx + max_length]
															__lowercase					     =				self._np_extract_fbank_features(lowerCAmelCase__ ,  self.mel_filters_slaney		)[None, :]
											elif truncation == "fusion":
															__lowercase					     =				self._np_extract_fbank_features(lowerCAmelCase__ ,  self.mel_filters		)
															__lowercase					     =				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
															__lowercase					     =				mel.shape[0]
															if chunk_frames == total_frames:
																			# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																			# In this case, we just use the whole audio.
																			__lowercase					     =				np.stack([mel, mel, mel, mel] ,  axis=0		)
																			__lowercase					     =				False
															else:
																			__lowercase					     =				self._random_mel_fusion(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
																			__lowercase					     =				True
											else:
															raise NotImplementedError(F"data_truncating {truncation} not implemented"		)

							else:
											__lowercase					     =				False
											# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
											if waveform.shape[0] < max_length:
															if padding == "repeat":
																			__lowercase					     =				int(max_length / len(lowerCAmelCase__		)		)
																			__lowercase					     =				np.stack(np.tile(lowerCAmelCase__ ,  n_repeat + 1		)		)[:max_length]
															if padding == "repeatpad":
																			__lowercase					     =				int(max_length / len(lowerCAmelCase__		)		)
																			__lowercase					     =				np.stack(np.tile(lowerCAmelCase__ ,  lowerCAmelCase__		)		)
															__lowercase					     =				np.pad(lowerCAmelCase__ ,  (0, max_length - waveform.shape[0]) ,  mode='''constant''' ,  constant_values=0		)

											if truncation == "fusion":
															__lowercase					     =				self._np_extract_fbank_features(lowerCAmelCase__ ,  self.mel_filters		)
															__lowercase					     =				np.stack([input_mel, input_mel, input_mel, input_mel] ,  axis=0		)
											else:
															__lowercase					     =				self._np_extract_fbank_features(lowerCAmelCase__ ,  self.mel_filters_slaney		)[None, :]

							return input_mel, longer





			def __call__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  **lowerCAmelCase__ ,  )	->   BatchFeature:

							'''simple docstring'''


							__lowercase					     =				truncation if truncation is not None else self.truncation
							__lowercase					     =				padding if padding else self.padding

							if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
															raise ValueError(
															    F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
															    F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
															    F" was sampled with {self.sampling_rate} and not {sampling_rate}."		)
							else:
											logger.warning(
											    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
											    '''Failing to do so can result in silent errors that might be hard to debug.'''		)

							__lowercase					     =				isinstance(lowerCAmelCase__ ,  np.ndarray		) and len(raw_speech.shape		) > 1
							if is_batched_numpy and len(raw_speech.shape		) > 2:
											raise ValueError(F"Only mono-channel audio is supported for input to {self}"		)
							__lowercase					     =				is_batched_numpy or (
							    isinstance(lowerCAmelCase__ ,  (list, tuple)		) and (isinstance(raw_speech[0] ,  (np.ndarray, tuple, list)		))
							)

							if is_batched:
											__lowercase					     =				[np.asarray(lowerCAmelCase__ ,  dtype=np.floataa		) for speech in raw_speech]
							elif not is_batched and not isinstance(lowerCAmelCase__ ,  np.ndarray		):
											__lowercase					     =				np.asarray(lowerCAmelCase__ ,  dtype=np.floataa		)
							elif isinstance(lowerCAmelCase__ ,  np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
											__lowercase					     =				raw_speech.astype(np.floataa		)

							# always return batch
							if not is_batched:
											__lowercase					     =				[np.asarray(lowerCAmelCase__		)]

							# convert to mel spectrogram, truncate and pad if needed.
							__lowercase					     =				[
							    self._get_input_mel(lowerCAmelCase__ ,  max_length if max_length else self.nb_max_samples ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
							    for waveform in raw_speech
							]

							__lowercase					     =				[]
							__lowercase					     =				[]
							for mel, longer in padded_inputs:
											input_mel.append(lowerCAmelCase__		)
											is_longer.append(lowerCAmelCase__		)

							if truncation == "fusion" and sum(lowerCAmelCase__		) == 0:
											# if no audio is longer than 10s, then randomly select one audio to be longer
											__lowercase					     =				np.random.randint(0 ,  len(lowerCAmelCase__		)		)
											__lowercase					     =				True

							if isinstance(input_mel[0] ,  lowerCAmelCase__		):
											__lowercase					     =				[np.asarray(lowerCAmelCase__ ,  dtype=np.floataa		) for feature in input_mel]

							# is_longer is a list of bool
							__lowercase					     =				[[longer] for longer in is_longer]

							__lowercase					     =				{'''input_features''': input_mel, '''is_longer''': is_longer}
							__lowercase					     =				BatchFeature(lowerCAmelCase__		)

							if return_tensors is not None:
											__lowercase					     =				input_features.convert_to_tensors(lowerCAmelCase__		)

							return input_features
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings